Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	130,313
2	South Dakota	125,815
3	Rhode Island	116,244
4	Utah	114,391
5	Arizona	111,042
6	Tennessee	110,066
7	Oklahoma	105,978
8	Iowa	105,430
9	Wisconsin	105,151
10	Arkansas	104,550
11	Nebraska	103,060
12	Kansas	100,919
13	Alabama	99,291
14	South Carolina	97,917
15	Indiana	97,847
16	Mississippi	97,653
17	Idaho	94,696
18	Nevada	94,484
19	Wyoming	92,948
20	Illinois	92,907
21	Montana	92,422
22	Louisiana	91,244
23	Georgia	90,483
24	Kentucky	89,745
25	Texas	89,675
26	California	89,376
27	New Mexico	87,174
28	Delaware	87,014
29	Florida	87,009
30	New Jersey	86,285
31	Minnesota	84,953
32	Missouri	83,594
33	Massachusetts	82,594
34	New York	81,832
35	Ohio	81,594
36	North Carolina	80,720
37	Alaska	77,888
38	Connecticut	76,600
39	Colorado	73,489
40	West Virginia	72,324
41	Pennsylvania	71,721
42	Virginia	66,090
43	Michigan	63,701
44	Maryland	62,281
45	District of Columbia	56,178
46	New Hampshire	53,991
47	Washington	44,248
48	Puerto Rico	41,550
49	Oregon	36,232
50	Maine	32,358
51	Vermont	23,226
52	Hawaii	19,122

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	589
2	New York	394
3	Kentucky	309
4	Delaware	301
5	New Jersey	298
6	Florida	295
7	North Carolina	270
8	Georgia	268
9	Massachusetts	256
10	New Hampshire	248
11	Oklahoma	242
12	Utah	242
13	Virginia	242
14	Kansas	234
15	Arizona	233
16	Texas	205
17	Pennsylvania	185
18	Vermont	183
19	Ohio	181
20	Colorado	180
21	Montana	177
22	Alabama	168
23	Tennessee	168
24	Louisiana	167
25	New Mexico	166
26	Indiana	165
27	District of Columbia	163
28	South Dakota	163
29	Minnesota	162
30	West Virginia	159
31	California	156
32	Wyoming	152
33	Nebraska	151
34	Illinois	147
35	Maryland	131
36	Wisconsin	125
37	Mississippi	123
38	Iowa	120
39	Arkansas	118
40	Nevada	113
41	Connecticut	112
42	North Dakota	106
43	Maine	100
44	Alaska	96
45	Rhode Island	89
46	Oregon	87
47	Missouri	84
48	Idaho	83
49	Michigan	74
50	Washington	70
51	Puerto Rico	60
52	Hawaii	39

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,573
2	New York	2,382
3	Massachusetts	2,296
4	Rhode Island	2,242
5	Mississippi	2,201
6	Arizona	2,130
7	Connecticut	2,110
8	South Dakota	2,105
9	Louisiana	2,036
10	Alabama	1,956
11	North Dakota	1,926
12	Pennsylvania	1,846
13	Indiana	1,837
14	Arkansas	1,775
15	Illinois	1,772
16	New Mexico	1,729
17	Iowa	1,691
18	Michigan	1,635
19	South Carolina	1,616
20	Tennessee	1,616
21	Kansas	1,583
22	Nevada	1,581
23	Georgia	1,529
24	Texas	1,459
25	Ohio	1,438
26	District of Columbia	1,409
27	Delaware	1,403
28	Florida	1,392
29	Missouri	1,328
30	Maryland	1,276
31	West Virginia	1,261
32	Montana	1,256
33	California	1,248
34	Wisconsin	1,180
35	Minnesota	1,152
36	Wyoming	1,143
37	Nebraska	1,121
38	Oklahoma	1,056
39	North Carolina	1,044
40	Colorado	1,039
41	Kentucky	1,030
42	Idaho	1,023
43	Virginia	858
44	New Hampshire	848
45	Washington	641
46	Puerto Rico	619
47	Utah	577
48	Oregon	511
49	Maine	489
50	Alaska	380
51	Vermont	315
52	Hawaii	302

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Delaware	14
2	Alabama	11
3	Georgia	10
4	Kansas	10
5	South Carolina	10
6	Arizona	9
7	Virginia	9
8	California	8
9	Nevada	7
10	Florida	6
11	Massachusetts	6
12	New Mexico	6
13	New York	6
14	South Dakota	6
15	Texas	6
16	New Jersey	5
17	Ohio	5
18	Oklahoma	5
19	Illinois	4
20	Indiana	4
21	Kentucky	4
22	Louisiana	4
23	North Carolina	4
24	Pennsylvania	4
25	Puerto Rico	4
26	Utah	4
27	West Virginia	4
28	Arkansas	3
29	Missouri	3
30	Nebraska	3
31	Tennessee	3
32	Wisconsin	3
33	Connecticut	2
34	Maryland	2
35	Michigan	2
36	Mississippi	2
37	Rhode Island	2
38	Vermont	2
39	Colorado	1
40	District of Columbia	1
41	Iowa	1
42	Minnesota	1
43	Montana	1
44	North Dakota	1
45	Alaska	0
46	Hawaii	0
47	Idaho	0
48	Maine	0
49	New Hampshire	0
50	Oregon	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	323,214	1	99
Chattahoochee	Georgia	278,628	2	99
Bent	Colorado	263,403	3	99
Lincoln	Arkansas	240,172	4	99
Lake	Tennessee	238,170	5	99
Davidson	Tennessee	127,485	230	92
Richland	South Carolina	97,102	1131	64
York	South Carolina	91,010	1395	55
Orange	California	81,653	1853	41
Pierce	Washington	41,519	2903	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Foard	Texas	6,926	3	99
Iron	Wisconsin	6,858	4	99
Galax city	Virginia	6,775	5	99
Orange	California	1,200	1935	38
Davidson	Tennessee	1,196	1944	38
Richland	South Carolina	1,174	1967	37
York	South Carolina	1,121	2043	34
Pierce	Washington	582	2695	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.